Overview

Dataset statistics

Number of variables8
Number of observations414
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory26.0 KiB
Average record size in memory64.3 B

Variable types

Numeric8

Alerts

X3 distance to the nearest MRT station is highly overall correlated with X4 number of convenience stores and 1 other fieldsHigh correlation
X4 number of convenience stores is highly overall correlated with X3 distance to the nearest MRT station and 1 other fieldsHigh correlation
X5 latitude is highly overall correlated with Y house price of unit areaHigh correlation
Y house price of unit area is highly overall correlated with X3 distance to the nearest MRT station and 2 other fieldsHigh correlation
No is uniformly distributedUniform
No has unique valuesUnique
X2 house age has 17 (4.1%) zerosZeros
X4 number of convenience stores has 67 (16.2%) zerosZeros

Reproduction

Analysis started2024-04-23 05:27:29.062242
Analysis finished2024-04-23 05:27:47.901285
Duration18.84 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

No
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct414
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean207.5
Minimum1
Maximum414
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2024-04-23T10:57:48.222975image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile21.65
Q1104.25
median207.5
Q3310.75
95-th percentile393.35
Maximum414
Range413
Interquartile range (IQR)206.5

Descriptive statistics

Standard deviation119.65576
Coefficient of variation (CV)0.57665425
Kurtosis-1.2
Mean207.5
Median Absolute Deviation (MAD)103.5
Skewness0
Sum85905
Variance14317.5
MonotonicityStrictly increasing
2024-04-23T10:57:48.616246image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.2%
273 1
 
0.2%
283 1
 
0.2%
282 1
 
0.2%
281 1
 
0.2%
280 1
 
0.2%
279 1
 
0.2%
278 1
 
0.2%
277 1
 
0.2%
276 1
 
0.2%
Other values (404) 404
97.6%
ValueCountFrequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
414 1
0.2%
413 1
0.2%
412 1
0.2%
411 1
0.2%
410 1
0.2%
409 1
0.2%
408 1
0.2%
407 1
0.2%
406 1
0.2%
405 1
0.2%

X1 transaction date
Real number (ℝ)

Distinct12
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013.149
Minimum2012.667
Maximum2013.583
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2024-04-23T10:57:48.946972image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum2012.667
5-th percentile2012.667
Q12012.917
median2013.167
Q32013.417
95-th percentile2013.583
Maximum2013.583
Range0.916
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.28196724
Coefficient of variation (CV)0.00014006278
Kurtosis-1.2331624
Mean2013.149
Median Absolute Deviation (MAD)0.25
Skewness-0.15057179
Sum833443.67
Variance0.079505525
MonotonicityNot monotonic
2024-04-23T10:57:49.269344image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2013.417 58
14.0%
2013.5 47
11.4%
2013.083 46
11.1%
2012.917 38
9.2%
2013.25 32
7.7%
2012.833 31
7.5%
2012.667 30
7.2%
2013.333 29
7.0%
2013 28
6.8%
2012.75 27
6.5%
Other values (2) 48
11.6%
ValueCountFrequency (%)
2012.667 30
7.2%
2012.75 27
6.5%
2012.833 31
7.5%
2012.917 38
9.2%
2013 28
6.8%
2013.083 46
11.1%
2013.167 25
6.0%
2013.25 32
7.7%
2013.333 29
7.0%
2013.417 58
14.0%
ValueCountFrequency (%)
2013.583 23
 
5.6%
2013.5 47
11.4%
2013.417 58
14.0%
2013.333 29
7.0%
2013.25 32
7.7%
2013.167 25
6.0%
2013.083 46
11.1%
2013 28
6.8%
2012.917 38
9.2%
2012.833 31
7.5%

X2 house age
Real number (ℝ)

ZEROS 

Distinct236
Distinct (%)57.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.71256
Minimum0
Maximum43.8
Zeros17
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2024-04-23T10:57:49.637005image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.1
Q19.025
median16.1
Q328.15
95-th percentile37.735
Maximum43.8
Range43.8
Interquartile range (IQR)19.125

Descriptive statistics

Standard deviation11.392485
Coefficient of variation (CV)0.64318677
Kurtosis-0.87712011
Mean17.71256
Median Absolute Deviation (MAD)8.4
Skewness0.38292623
Sum7333
Variance129.7887
MonotonicityNot monotonic
2024-04-23T10:57:50.031926image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 17
 
4.1%
13.6 7
 
1.7%
13.3 6
 
1.4%
16.2 6
 
1.4%
16.4 6
 
1.4%
13.2 6
 
1.4%
16.9 5
 
1.2%
1.1 5
 
1.2%
18 4
 
1.0%
13 4
 
1.0%
Other values (226) 348
84.1%
ValueCountFrequency (%)
0 17
4.1%
1 1
 
0.2%
1.1 5
 
1.2%
1.5 2
 
0.5%
1.7 1
 
0.2%
1.8 1
 
0.2%
1.9 1
 
0.2%
2 2
 
0.5%
2.1 1
 
0.2%
2.3 1
 
0.2%
ValueCountFrequency (%)
43.8 1
0.2%
42.7 1
0.2%
41.4 1
0.2%
41.3 2
0.5%
40.9 2
0.5%
40.1 1
0.2%
39.8 1
0.2%
39.7 1
0.2%
39.6 1
0.2%
39.2 1
0.2%

X3 distance to the nearest MRT station
Real number (ℝ)

HIGH CORRELATION 

Distinct259
Distinct (%)62.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1083.8857
Minimum23.38284
Maximum6488.021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2024-04-23T10:57:50.420248image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum23.38284
5-th percentile90.45606
Q1289.3248
median492.2313
Q31454.279
95-th percentile4082.015
Maximum6488.021
Range6464.6382
Interquartile range (IQR)1164.9542

Descriptive statistics

Standard deviation1262.1096
Coefficient of variation (CV)1.1644305
Kurtosis3.2078684
Mean1083.8857
Median Absolute Deviation (MAD)307.3514
Skewness1.8887566
Sum448728.68
Variance1592920.6
MonotonicityNot monotonic
2024-04-23T10:57:50.975831image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
289.3248 13
 
3.1%
90.45606 11
 
2.7%
492.2313 9
 
2.2%
1360.139 8
 
1.9%
104.8101 8
 
1.9%
4082.015 7
 
1.7%
390.5684 6
 
1.4%
2147.376 6
 
1.4%
4066.587 6
 
1.4%
193.5845 5
 
1.2%
Other values (249) 335
80.9%
ValueCountFrequency (%)
23.38284 2
 
0.5%
49.66105 2
 
0.5%
56.47425 3
 
0.7%
57.58945 1
 
0.2%
82.88643 1
 
0.2%
84.87882 1
 
0.2%
87.30222 1
 
0.2%
90.45606 11
2.7%
104.8101 8
1.9%
109.9455 1
 
0.2%
ValueCountFrequency (%)
6488.021 1
0.2%
6396.283 1
0.2%
6306.153 1
0.2%
5512.038 2
0.5%
4605.749 1
0.2%
4573.779 1
0.2%
4527.687 1
0.2%
4519.69 2
0.5%
4510.359 2
0.5%
4449.27 1
0.2%

X4 number of convenience stores
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0942029
Minimum0
Maximum10
Zeros67
Zeros (%)16.2%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2024-04-23T10:57:51.317954image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q36
95-th percentile9
Maximum10
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.9455618
Coefficient of variation (CV)0.71944695
Kurtosis-1.0657515
Mean4.0942029
Median Absolute Deviation (MAD)3
Skewness0.15460657
Sum1695
Variance8.6763344
MonotonicityNot monotonic
2024-04-23T10:57:51.625149image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
5 67
16.2%
0 67
16.2%
3 46
11.1%
1 46
11.1%
6 37
8.9%
7 31
7.5%
4 31
7.5%
8 30
7.2%
9 25
 
6.0%
2 24
 
5.8%
ValueCountFrequency (%)
0 67
16.2%
1 46
11.1%
2 24
 
5.8%
3 46
11.1%
4 31
7.5%
5 67
16.2%
6 37
8.9%
7 31
7.5%
8 30
7.2%
9 25
 
6.0%
ValueCountFrequency (%)
10 10
 
2.4%
9 25
 
6.0%
8 30
7.2%
7 31
7.5%
6 37
8.9%
5 67
16.2%
4 31
7.5%
3 46
11.1%
2 24
 
5.8%
1 46
11.1%

X5 latitude
Real number (ℝ)

HIGH CORRELATION 

Distinct234
Distinct (%)56.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.96903
Minimum24.93207
Maximum25.01459
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2024-04-23T10:57:51.986745image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum24.93207
5-th percentile24.945759
Q124.963
median24.9711
Q324.977455
95-th percentile24.985704
Maximum25.01459
Range0.08252
Interquartile range (IQR)0.014455

Descriptive statistics

Standard deviation0.012410197
Coefficient of variation (CV)0.00049702357
Kurtosis0.26906978
Mean24.96903
Median Absolute Deviation (MAD)0.00787
Skewness-0.43859845
Sum10337.178
Variance0.00015401298
MonotonicityNot monotonic
2024-04-23T10:57:52.383355image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.97433 14
 
3.4%
24.98203 13
 
3.1%
24.96674 9
 
2.2%
24.96515 9
 
2.2%
24.96299 8
 
1.9%
24.95204 8
 
1.9%
24.94155 7
 
1.7%
24.97937 6
 
1.4%
24.94297 6
 
1.4%
24.9711 5
 
1.2%
Other values (224) 329
79.5%
ValueCountFrequency (%)
24.93207 1
 
0.2%
24.93293 1
 
0.2%
24.93363 1
 
0.2%
24.93885 3
0.7%
24.94155 7
1.7%
24.94235 1
 
0.2%
24.94297 6
1.4%
24.94375 1
 
0.2%
24.94684 1
 
0.2%
24.94741 1
 
0.2%
ValueCountFrequency (%)
25.01459 1
 
0.2%
25.00115 1
 
0.2%
24.998 1
 
0.2%
24.99176 2
0.5%
24.99156 2
0.5%
24.99006 1
 
0.2%
24.98872 4
1.0%
24.98748 1
 
0.2%
24.98746 4
1.0%
24.98674 1
 
0.2%

X6 longitude
Real number (ℝ)

Distinct232
Distinct (%)56.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121.53336
Minimum121.47353
Maximum121.56627
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2024-04-23T10:57:52.654833image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum121.47353
5-th percentile121.50342
Q1121.52809
median121.53863
Q3121.54331
95-th percentile121.54905
Maximum121.56627
Range0.09274
Interquartile range (IQR)0.01522

Descriptive statistics

Standard deviation0.015347183
Coefficient of variation (CV)0.00012627959
Kurtosis1.2017925
Mean121.53336
Median Absolute Deviation (MAD)0.005805
Skewness-1.2195915
Sum50314.811
Variance0.00023553603
MonotonicityNot monotonic
2024-04-23T10:57:52.853040image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
121.54348 13
 
3.1%
121.5431 11
 
2.7%
121.53737 9
 
2.2%
121.54391 8
 
1.9%
121.54067 8
 
1.9%
121.54842 8
 
1.9%
121.50381 7
 
1.7%
121.54245 7
 
1.7%
121.50342 6
 
1.4%
121.54089 6
 
1.4%
Other values (222) 331
80.0%
ValueCountFrequency (%)
121.47353 1
 
0.2%
121.47516 1
 
0.2%
121.47883 1
 
0.2%
121.48458 2
0.5%
121.49507 1
 
0.2%
121.49542 2
0.5%
121.49578 1
 
0.2%
121.49587 3
0.7%
121.49621 1
 
0.2%
121.49628 1
 
0.2%
ValueCountFrequency (%)
121.56627 2
0.5%
121.56174 1
0.2%
121.55964 2
0.5%
121.55481 1
0.2%
121.55391 1
0.2%
121.55387 1
0.2%
121.55282 1
0.2%
121.55254 1
0.2%
121.55174 1
0.2%
121.55063 1
0.2%

Y house price of unit area
Real number (ℝ)

HIGH CORRELATION 

Distinct270
Distinct (%)65.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.980193
Minimum7.6
Maximum117.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2024-04-23T10:57:53.157439image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum7.6
5-th percentile16.49
Q127.7
median38.45
Q346.6
95-th percentile59.175
Maximum117.5
Range109.9
Interquartile range (IQR)18.9

Descriptive statistics

Standard deviation13.606488
Coefficient of variation (CV)0.3582522
Kurtosis2.179097
Mean37.980193
Median Absolute Deviation (MAD)9.35
Skewness0.59985258
Sum15723.8
Variance185.13651
MonotonicityNot monotonic
2024-04-23T10:57:53.329823image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42.5 4
 
1.0%
40.3 4
 
1.0%
29.3 4
 
1.0%
40.6 4
 
1.0%
37.4 4
 
1.0%
42 4
 
1.0%
37.5 4
 
1.0%
24.7 4
 
1.0%
31.3 4
 
1.0%
42.3 4
 
1.0%
Other values (260) 374
90.3%
ValueCountFrequency (%)
7.6 1
0.2%
11.2 1
0.2%
11.6 1
0.2%
12.2 1
0.2%
12.8 2
0.5%
12.9 1
0.2%
13 1
0.2%
13.2 1
0.2%
13.4 1
0.2%
13.7 1
0.2%
ValueCountFrequency (%)
117.5 1
0.2%
78.3 1
0.2%
78 1
0.2%
73.6 1
0.2%
71 1
0.2%
70.1 1
0.2%
69.7 1
0.2%
67.7 1
0.2%
63.9 1
0.2%
63.3 2
0.5%

Interactions

2024-04-23T10:57:44.712966image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:29.566605image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:31.865864image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:33.993786image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:36.051799image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:38.254990image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:40.395709image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:42.467505image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:44.966568image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:29.830524image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:32.117745image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:34.237967image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:36.305568image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:38.494807image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:40.654890image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:42.717288image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:45.245506image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:30.091927image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:32.373291image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:34.501778image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:36.588170image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:38.762553image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:40.909373image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:42.996706image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:45.501874image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:30.341998image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:32.741451image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:34.741525image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:36.861034image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:39.025020image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:41.152415image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:43.394170image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:45.791679image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:30.624156image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:32.952291image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:35.023458image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:37.135839image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:39.315509image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:41.428991image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:43.536400image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:46.069880image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:30.872120image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:33.219140image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:35.284558image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:37.427258image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:39.618434image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:41.705476image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:43.792104image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:46.319620image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:31.206501image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:33.464333image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:35.532444image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:37.688936image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:39.863550image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:41.952580image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:44.175866image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:46.592176image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:31.579557image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:33.727590image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:35.791128image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:37.973200image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:40.126428image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:42.205241image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-23T10:57:44.428836image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Correlations

2024-04-23T10:57:53.441563image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
NoX1 transaction dateX2 house ageX3 distance to the nearest MRT stationX4 number of convenience storesX5 latitudeX6 longitudeY house price of unit area
No1.000-0.056-0.028-0.032-0.020-0.021-0.043-0.048
X1 transaction date-0.0561.0000.0350.0930.0030.024-0.0140.067
X2 house age-0.0280.0351.0000.1300.0090.042-0.110-0.282
X3 distance to the nearest MRT station-0.0320.0930.1301.000-0.688-0.425-0.469-0.776
X4 number of convenience stores-0.0200.0030.009-0.6881.0000.4290.4120.617
X5 latitude-0.0210.0240.042-0.4250.4291.0000.2650.578
X6 longitude-0.043-0.014-0.110-0.4690.4120.2651.0000.438
Y house price of unit area-0.0480.067-0.282-0.7760.6170.5780.4381.000

Missing values

2024-04-23T10:57:46.978672image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-23T10:57:47.507121image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

NoX1 transaction dateX2 house ageX3 distance to the nearest MRT stationX4 number of convenience storesX5 latitudeX6 longitudeY house price of unit area
012012.91732.084.878821024.98298121.5402437.9
122012.91719.5306.59470924.98034121.5395142.2
232013.58313.3561.98450524.98746121.5439147.3
342013.50013.3561.98450524.98746121.5439154.8
452012.8335.0390.56840524.97937121.5424543.1
562012.6677.12175.03000324.96305121.5125432.1
672012.66734.5623.47310724.97933121.5364240.3
782013.41720.3287.60250624.98042121.5422846.7
892013.50031.75512.03800124.95095121.4845818.8
9102013.41717.91783.18000324.96731121.5148622.1
NoX1 transaction dateX2 house ageX3 distance to the nearest MRT stationX4 number of convenience storesX5 latitudeX6 longitudeY house price of unit area
4044052013.33316.4289.32480524.98203121.5434841.2
4054062012.66723.0130.99450624.95663121.5376537.2
4064072013.1671.9372.13860724.97293121.5402640.5
4074082013.0005.22408.99300024.95505121.5596422.3
4084092013.41718.52175.74400324.96330121.5124328.1
4094102013.00013.74082.01500024.94155121.5038115.4
4104112012.6675.690.45606924.97433121.5431050.0
4114122013.25018.8390.96960724.97923121.5398640.6
4124132013.0008.1104.81010524.96674121.5406752.5
4134142013.5006.590.45606924.97433121.5431063.9